College of Computer and Control Engineering, Qiqihar University, Qiqihar 161003, China.
Sensors (Basel). 2021 Nov 22;21(22):7762. doi: 10.3390/s21227762.
Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers.
与耗时且不可靠的手动分析相比，使用深度学习模型的智能故障诊断技术可以通过其多层非线性映射能力提高智能故障诊断的准确性。本文提出了一种使用振动传感器时间序列作为输入进行故障诊断和分类的模型。该模型将原始振动信号编码为二维图像，并通过深度卷积神经网络或改进的胶囊网络进行特征提取和分类。提出了一种基于 Gramian Angular Field（GAF）、Markov Transition Field（MTF）和胶囊网络的故障诊断技术。在凯斯西储大学的轴承故障数据集上进行的实验研究了两种编码方法和不同网络结构对诊断精度的影响。结果表明，GAF 技术保留了更完整的故障特征，而 MTF 技术包含少量的故障特征但更多的动态特征。因此，所提出的方法将 GAF 图像和 MTF 图像合并为胶囊网络的双通道图像输入，使网络能够获得更完整的故障特征。在凯斯西储大学的轴承故障数据集上进行了多组实验，所提出模型中的胶囊网络优于其他卷积神经网络，并且在与其他研究人员提出的故障诊断方法的比较中表现良好。
